惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

量子位
Vercel News
Vercel News
Microsoft Azure Blog
Microsoft Azure Blog
爱范儿
爱范儿
N
Netflix TechBlog - Medium
Google DeepMind News
Google DeepMind News
H
Help Net Security
罗磊的独立博客
The Cloudflare Blog
J
Java Code Geeks
博客园 - 叶小钗
I
InfoQ
B
Blog
Blog — PlanetScale
Blog — PlanetScale
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
腾讯CDC
月光博客
月光博客
博客园_首页
雷峰网
雷峰网
M
MIT News - Artificial intelligence
博客园 - 【当耐特】
美团技术团队
T
The Blog of Author Tim Ferriss
博客园 - 司徒正美

cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
SISSLE in consensus-based Ripple: Some Improvements in Sp...
Mayank Mundhra, Chester Rebeiro · 2020-08-05 · via cs.DC updates on arXiv.org

Cryptocurrencies are rapidly finding application in areas such as Real Time Gross Settlements and Payments. Ripple is a cryptocurrency that has gained prominence with banks and payment providers. It solves the Byzantine General's Problem with its Ripple Protocol Consensus Algorithm (RPCA), where each server maintains a list of servers, called the Unique Node List (UNL), that represents the network for that server and will not collectively defraud it. The server believes that the network has come to a consensus when servers on the UNL come to a consensus on a transaction. In this paper we improve Ripple to achieve better speed, security and last mile connectivity. We implement guidelines for resilience, robustness, improved security, and efficient information propagation (IP). We enhance the system to ensure that each server receives information from across the whole network rather than just from the UNL members. We introduce the paradigm of UNL overlap as a function of IP and the trust a server assigns to its own UNL. Our design makes it possible to identify and mitigate some malicious behaviours including attempts to fraudulently Double Spend or stall the system. We provide experimental evidence of the benefits of our approach over the current Ripple scheme. We observe $\geq 99.67\%$ reduction in opportunities for double spend attacks and censorship, $1.71x$ increase in fault tolerance to $\geq 34.21\%$ malicious nodes, $\geq 4.97x$ and $98.22x$ speedup and success rate for IP respectively, and $\geq 3.16x$ and $51.70x$ speedup and success rate in consensus respectively.